GWAS and systems biology analysis of depressive symptoms among smokers from the COPDGene cohort

COPDGene Investigators, Jonathan T. Heinzman, Karin F. Hoth, Michael H. Cho, Phuwanat Sakornsakolpat, Elizabeth A. Regan, Barry J. Make, Gregory L. Kinney, Frederick S. Wamboldt, Kristen E. Holm, Nicholas Bormann, Julian Robles, Victor Kim, Anand S. Iyer, Edwin K. Silverman, James D. Crapo, Shizhong Han, James Bennett Potash, Gen Shinozaki

Research output: Contribution to journalArticle

Abstract

Background: Large sample GWAS is needed to identify genetic factors associated with depression. This study used genome-wide genotypic and phenotypic data from the COPDGene study to identify genetic risk factors for depression. Methods: Data were from 9716 COPDGene subjects with ≥10 pack-year history. Depression was defined as antidepressant use and/or a HADS depression subscale score ≥8. Non-Hispanic White (6576) and African-American (3140) subsets were analyzed. A GWAS pipeline identified SNPs associated with depression in each group. Network analysis software analyzed gene interactions through common biological pathways, genetic interactions, and tissue-specific gene expression. Results: The mean age was 59.4 years (SD 9.0) with 46.5% female subjects. Depression was in 24.7% of the NHW group (1622) and 12.5% of the AA group (391). No SNPs had genome-wide significance. One of the top SNPs, rs12036147 (p = 1.28 × 10−6), is near CHRM3. Another SNP was near MDGA2 (rs17118176, p = 3.52 × 10−6). Top genes formed networks for synaptic transmission with a statistically significant level of more co-expression in brain than other tissues, particularly in the basal ganglia (p = 1.00 × 10−4). Limitations: Limitations included a depression definition based on antidepressant use and a limited HADS score subgroup, which could increase false negatives in depressed patients not on antidepressants. Antidepressants used for smoking cessation in non-depressed patients could lead to false positives. Conclusions: Systems biology analysis identified statistically significant pathways whereby multiple genes influence depression. The gene set pathway analysis and COPDGene data can help investigate depression in future studies.

LanguageEnglish (US)
Pages16-22
Number of pages7
JournalJournal of Affective Disorders
Volume243
DOIs
StatePublished - Jan 15 2019

Fingerprint

Systems Biology
Genome-Wide Association Study
Depression
Antidepressive Agents
Single Nucleotide Polymorphism
Genome
Genes
Gene Regulatory Networks
Smoking Cessation
Basal Ganglia
Synaptic Transmission
African Americans
Software
History
Gene Expression
Brain

Keywords

  • Chronic obstructive pulmonary disease
  • Genome-wide association study
  • Major depressive disorder
  • Smokers
  • Systems biology

ASJC Scopus subject areas

  • Clinical Psychology
  • Psychiatry and Mental health

Cite this

GWAS and systems biology analysis of depressive symptoms among smokers from the COPDGene cohort. / COPDGene Investigators.

In: Journal of Affective Disorders, Vol. 243, 15.01.2019, p. 16-22.

Research output: Contribution to journalArticle

@article{a6bb604c2b8e448188b5e71a7653b477,
title = "GWAS and systems biology analysis of depressive symptoms among smokers from the COPDGene cohort",
abstract = "Background: Large sample GWAS is needed to identify genetic factors associated with depression. This study used genome-wide genotypic and phenotypic data from the COPDGene study to identify genetic risk factors for depression. Methods: Data were from 9716 COPDGene subjects with ≥10 pack-year history. Depression was defined as antidepressant use and/or a HADS depression subscale score ≥8. Non-Hispanic White (6576) and African-American (3140) subsets were analyzed. A GWAS pipeline identified SNPs associated with depression in each group. Network analysis software analyzed gene interactions through common biological pathways, genetic interactions, and tissue-specific gene expression. Results: The mean age was 59.4 years (SD 9.0) with 46.5{\%} female subjects. Depression was in 24.7{\%} of the NHW group (1622) and 12.5{\%} of the AA group (391). No SNPs had genome-wide significance. One of the top SNPs, rs12036147 (p = 1.28 × 10−6), is near CHRM3. Another SNP was near MDGA2 (rs17118176, p = 3.52 × 10−6). Top genes formed networks for synaptic transmission with a statistically significant level of more co-expression in brain than other tissues, particularly in the basal ganglia (p = 1.00 × 10−4). Limitations: Limitations included a depression definition based on antidepressant use and a limited HADS score subgroup, which could increase false negatives in depressed patients not on antidepressants. Antidepressants used for smoking cessation in non-depressed patients could lead to false positives. Conclusions: Systems biology analysis identified statistically significant pathways whereby multiple genes influence depression. The gene set pathway analysis and COPDGene data can help investigate depression in future studies.",
keywords = "Chronic obstructive pulmonary disease, Genome-wide association study, Major depressive disorder, Smokers, Systems biology",
author = "{COPDGene Investigators} and Heinzman, {Jonathan T.} and Hoth, {Karin F.} and Cho, {Michael H.} and Phuwanat Sakornsakolpat and Regan, {Elizabeth A.} and Make, {Barry J.} and Kinney, {Gregory L.} and Wamboldt, {Frederick S.} and Holm, {Kristen E.} and Nicholas Bormann and Julian Robles and Victor Kim and Iyer, {Anand S.} and Silverman, {Edwin K.} and Crapo, {James D.} and Shizhong Han and Potash, {James Bennett} and Gen Shinozaki",
year = "2019",
month = "1",
day = "15",
doi = "10.1016/j.jad.2018.09.003",
language = "English (US)",
volume = "243",
pages = "16--22",
journal = "Journal of Affective Disorders",
issn = "0165-0327",
publisher = "Elsevier",

}

TY - JOUR

T1 - GWAS and systems biology analysis of depressive symptoms among smokers from the COPDGene cohort

AU - COPDGene Investigators

AU - Heinzman, Jonathan T.

AU - Hoth, Karin F.

AU - Cho, Michael H.

AU - Sakornsakolpat, Phuwanat

AU - Regan, Elizabeth A.

AU - Make, Barry J.

AU - Kinney, Gregory L.

AU - Wamboldt, Frederick S.

AU - Holm, Kristen E.

AU - Bormann, Nicholas

AU - Robles, Julian

AU - Kim, Victor

AU - Iyer, Anand S.

AU - Silverman, Edwin K.

AU - Crapo, James D.

AU - Han, Shizhong

AU - Potash, James Bennett

AU - Shinozaki, Gen

PY - 2019/1/15

Y1 - 2019/1/15

N2 - Background: Large sample GWAS is needed to identify genetic factors associated with depression. This study used genome-wide genotypic and phenotypic data from the COPDGene study to identify genetic risk factors for depression. Methods: Data were from 9716 COPDGene subjects with ≥10 pack-year history. Depression was defined as antidepressant use and/or a HADS depression subscale score ≥8. Non-Hispanic White (6576) and African-American (3140) subsets were analyzed. A GWAS pipeline identified SNPs associated with depression in each group. Network analysis software analyzed gene interactions through common biological pathways, genetic interactions, and tissue-specific gene expression. Results: The mean age was 59.4 years (SD 9.0) with 46.5% female subjects. Depression was in 24.7% of the NHW group (1622) and 12.5% of the AA group (391). No SNPs had genome-wide significance. One of the top SNPs, rs12036147 (p = 1.28 × 10−6), is near CHRM3. Another SNP was near MDGA2 (rs17118176, p = 3.52 × 10−6). Top genes formed networks for synaptic transmission with a statistically significant level of more co-expression in brain than other tissues, particularly in the basal ganglia (p = 1.00 × 10−4). Limitations: Limitations included a depression definition based on antidepressant use and a limited HADS score subgroup, which could increase false negatives in depressed patients not on antidepressants. Antidepressants used for smoking cessation in non-depressed patients could lead to false positives. Conclusions: Systems biology analysis identified statistically significant pathways whereby multiple genes influence depression. The gene set pathway analysis and COPDGene data can help investigate depression in future studies.

AB - Background: Large sample GWAS is needed to identify genetic factors associated with depression. This study used genome-wide genotypic and phenotypic data from the COPDGene study to identify genetic risk factors for depression. Methods: Data were from 9716 COPDGene subjects with ≥10 pack-year history. Depression was defined as antidepressant use and/or a HADS depression subscale score ≥8. Non-Hispanic White (6576) and African-American (3140) subsets were analyzed. A GWAS pipeline identified SNPs associated with depression in each group. Network analysis software analyzed gene interactions through common biological pathways, genetic interactions, and tissue-specific gene expression. Results: The mean age was 59.4 years (SD 9.0) with 46.5% female subjects. Depression was in 24.7% of the NHW group (1622) and 12.5% of the AA group (391). No SNPs had genome-wide significance. One of the top SNPs, rs12036147 (p = 1.28 × 10−6), is near CHRM3. Another SNP was near MDGA2 (rs17118176, p = 3.52 × 10−6). Top genes formed networks for synaptic transmission with a statistically significant level of more co-expression in brain than other tissues, particularly in the basal ganglia (p = 1.00 × 10−4). Limitations: Limitations included a depression definition based on antidepressant use and a limited HADS score subgroup, which could increase false negatives in depressed patients not on antidepressants. Antidepressants used for smoking cessation in non-depressed patients could lead to false positives. Conclusions: Systems biology analysis identified statistically significant pathways whereby multiple genes influence depression. The gene set pathway analysis and COPDGene data can help investigate depression in future studies.

KW - Chronic obstructive pulmonary disease

KW - Genome-wide association study

KW - Major depressive disorder

KW - Smokers

KW - Systems biology

UR - http://www.scopus.com/inward/record.url?scp=85053210624&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85053210624&partnerID=8YFLogxK

U2 - 10.1016/j.jad.2018.09.003

DO - 10.1016/j.jad.2018.09.003

M3 - Article

VL - 243

SP - 16

EP - 22

JO - Journal of Affective Disorders

T2 - Journal of Affective Disorders

JF - Journal of Affective Disorders

SN - 0165-0327

ER -